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Facial Expression Recognition Based on PCD-CNN with Pose and Expression 认领 引用
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作者 Hongbin Dong Jin Xu Qiang Fu 《国际计算机前沿大会会议论文集》 EI 2020年第1期518-533,共16页
In order to achieve high recognition rate,most facial expression recognition(FER)methods generate sufficient labeled facial images based on generative adversarial networks(GAN)to train model.However,these methods do n... In order to achieve high recognition rate,most facial expression recognition(FER)methods generate sufficient labeled facial images based on generative adversarial networks(GAN)to train model.However,these methods do not estimate the facial pose before passing the images to the generator,which affects the quality of generated images.And mode collapse is prone to occur during the training process,leading to generate a single-style facial images.To solve these problems,a FER model is proposed based on pose conditioned dendritic convolution neural network(PCD-CNN)with pose and expression.Before passing the facial images to the generator,PCD-CNN was used to process facial images,effectively estimating the facial landmarks to detect face and disentangle the pose.In order to accelerate the training speed of the model,PCD-CNN was based on the ShuffleNet-v2 framework.Every landmark of facial image was modeled by a separate ShuffleNet-DeconvNet,maintaining better performance with fewer parameters.To solve the mode collapse during image generation,we theoretically analyzed the causes,and implemented mini-batch processing on the discriminator in the model and directly calculated the statistical characteristics of the mini-batch samples.Experiments were carried out on the Multi-PIE and BU-3DFE facial expression datasets.Compared with current advanced methods,our method achieves higher accuracy 93.08%,and the training process is more stable. 展开更多
关键词 Pose estimation Mode collapse Expression recognition
A new accelerating algorithm for multi-agent reinforcement learning 认领 引用 被引量：1
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作者 张汝波 仲宇 顾国昌 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第1期48-51,共4页
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learni... In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm. 展开更多
关键词 distributed reinforcement learning accelerating algorithm machine learning multi-agent system
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